What Makes an Entrepreneur?
David G. Blanchflower, Dartmouth College and
National Bureau of Economic Research
Andrew J. Oswald, University of Warwick
This article uses various micro data sets to study entrepreneurship.
Consistent with the existence of capital constraints on potential entrepreneurs, the estimates imply that the probability of self-employment depends positively upon whether the individual ever received
an inheritance or gift. When directly questioned in interview surveys,
potential entrepreneurs say that raising capital is their principal problem. Consistent with our theoretical model’s predictions, the selfemployed report higher levels of job and life satisfaction than employees. Childhood psychological test scores, however, are not
strongly correlated with later self-employment.
For many commentators this is the era of the entrepreneur. After years of neglect, those who start and manage
their own businesses are viewed as popular heroes. They
The first version of this article, Blanchflower and Oswald (1990b), dates back
to 1988 and was in one of its iterations NBER Working Paper no. 3252. We
thank the ESRC and the UK Department of Employment for financial assistance.
Steve Machin contributed a crucial piece of technical help on this article. Useful
suggestions were also made by participants in seminars at Cambridge (UK),
LSE, Harvard, Dartmouth, Aberdeen, Glasgow, Guelph, McMaster, Oxford, the
London Business School, Queen Mary and Westfield College, Swansea, Uppsala,
FIEF (Stockholm), and Warwick, and by Peter Abell, Graham Beaver, Joan
Beaver, Fran Blau, George Borjas, Simon Burgess, Mark Casson, Andrew Clark,
Robert Cressy, Peter Elias, Roger Gordon, Al Gustman, Jonathan Haskell, Tom
Holmes, Peter Johnson, Bruce Meyer, Chris Pissarides, Gavin Reid, David Storey,
Steve Venti, and Alex Zanello.
[ Journal of Labor Economics, 1998, vol. 16, no. 1]
q 1998 by The University of Chicago. All rights reserved.
0734-306X/98/1601-0002$02.50
26
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are seen as risk-takers and innovators who reject the
relative security of employment in large organizations
to create wealth and accumulate capital. Indeed, according to many, economic recovery . . . is largely dependent upon their ambitions and efforts. (ROBERT
GOFFEE and RICHARD SCASE 1987, p. 1)
I. Introduction
Most Western governments provide encouragement and tax breaks to
those who run small businesses. This appears to be because politicians
believe, for reasons not always clearly articulated, that there are undesirable impediments to the market supply of entrepreneurship. Despite media and political interest in this topic, however, economists have contributed relatively little to the debate about how the economy generates
successful small businesses. It has long been noted that economics textbooks largely ignore the role of the entrepreneur and say little about the
formation of the small enterprises that provide the beginnings of giant
corporations.
The simplest kind of entrepreneurship is self-employment. There is
recent survey evidence to suggest that, in the industrialized countries,
many individuals who are currently employees would prefer to be selfemployed. Although it cannot be definitive, this evidence suggests that
there may be restrictions on the supply of entrepreneurs. For example,
the International Social Survey Programme of 1989 asked random samples
of individuals from 11 countries the question: ‘‘Suppose you were working and could choose between different kinds of jobs. Which of the
following would you choose? I would choose (i) Being an employee,
(ii) Being self-employed, (iii) Can’t choose.’’ Large numbers of people
gave answer ii and thus stated that they would wish to be self-employed.
This answer was given by, for example, a remarkable 63% of Americans
(out of 1,453 asked), 48% of Britons (out of 1,297), and 49% of Germans
(out of 1,575). These numbers can be compared with an actual proportion
of self-employed people in these countries of approximately 15%.
The data raise a puzzle: Why do not more of these individuals follow
their apparent desire to run their own business? In this article, we explore
the factors that may be important in determining who becomes and remains an entrepreneur. After years of comparative neglect, research on
the economics of entrepreneurship—especially upon self-employment—
is beginning to expand. Microeconometric work includes Fuchs (1982)
and Rees and Shah (1986) and, more recently, Pickles and O’Farrell
(1987), Borjas and Bronars (1989), Evans and Jovanovic (1989), and
Evans and Leighton (1989).1 This article follows in the general spirit of
1
OECD (1986) and Blau (1987) are aggregate time-series studies. Theoretical
analysis relevant to this article’s results includes Rosen (1983), Shorrocks (1988),
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Blanchflower/Oswald
these inquiries, although its data and methods differ from those in earlier
investigations.
One possible impediment to entrepreneurship is lack of capital. In
recent work using U.S. micro data, Evans and Leighton (1989) and Evans
and Jovanovic (1989) have argued formally that entrepreneurs face liquidity constraints. The authors use the National Longitudinal Survey of
Young Men for 1966–81 and the Current Population Surveys for 1968–
87. The key test shows that, all else equal, people with greater family
assets are more likely to switch to self-employment from employment.
This asset variable enters probit equations significantly and with a quadratic form. Although Evans and his collaborators draw the conclusion
that capital and liquidity constraints bind, this claim is open to the objection that other interpretations of their correlation are feasible. One possibility, for example, is that inherently acquisitive individuals both start
their own businesses and forgo leisure to build up family assets. In this
case, there would be a correlation between family assets and movement
into self-employment even if capital constraints did not exist. A second
possibility is that the correlation between family assets and the movement
to self-employment arises because children tend to inherit family firms.
The article provides, in Section IV, a new test of the finance-constraint
hypothesis. The test uses data on inheritances and gifts. Studying the
behavior of those who receive money is presumably as close as the economist can get to the idealized laboratory experiment in which some subjects
are issued capital while those in a control group get none. Results described later show that individuals who have received inheritances or gifts
are more likely to run their own businesses. This is true holding constant
a group of personal, family, and geographical characteristics. The effect
is large and is not the result of offspring inheriting family enterprises.
The receipt of an inheritance is not an entirely random event, so this
study does not provide a perfect test. Individuals who receive them may
come from backgrounds that make those people, for some unmeasured
reasons, prone to self-employment. Moreover, those who receive inheritances within families may be those who are—again for unmeasured
reasons—better suited to self-employment. It is not possible, without a
true experiment, to assuage all such concerns. However, the article presents various complementary forms of evidence for its claims. One draws
upon questionnaires. Although simple, this is of a kind apparently not
Casson (1990), and Holmes and Schmitz (1990). New empirical papers include
Reid and Jacobsen (1988), De Witt and Van Winden (1990), Lentz and Laband
(1990), Meyer (1990), Reid (1990), Holmes and Schmitz (1991), and Blanchflower and Meyer (1994). Work by Black, de Meza, and Jeffreys (1996) finds,
consistent with the general tenor of the approach taken here, that house asset
values play an important role in shaping the supply of entrepreneurs.
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reported before in the literature. Data from interviews with random samples of individuals demonstrate that, when asked, the self-employed say
that they are constrained principally by a lack of capital. Moreover, many
of those who are not self-employed say that it is predominantly a shortage
of capital that prevents them from starting their own business. Section V
describes this survey material. Although such survey responses have to
be interpreted with caution, the message they provide is consistent with
that from the quite different econometric methods.
Another theme within the article is the role of psychological characteristics. The analysis studies the correlation between the probability of
being self-employed as an adult and the individual’s childhood scores on
a number of psychological tests. Although originally a major motivation
for the research, the results are relatively disappointing. Individuals’ psychology—at least using the data available here—does not seem to play
a large role.
If it is true that capital and other constraints hold back the effective
supply of entrepreneurship, and so lead to there being frustrated employees who would rather be entrepreneurs, those who run their own businesses might be expected to be ‘‘happier,’’ on average, than those who
do not. In Section VI, we suggest and implement an econometric test of
this hypothesis, using data of a kind more commonly studied by psychologists.
II. Theoretical Background
Consider the following theoretical model in which people choose between working in the entrepreneurial sector and being an employee. First,
assume, following Knight (1921) and others, that entrepreneurial opportunities cannot be assigned probabilities. This might be thought to be
because the expected returns from new ideas and openings are inherently
impossible to quantify. Second, assume that entrepreneurs may be constrained in the amount of capital they can directly acquire. Consider
person j, who by assumption is a potential businessperson with the vision
to see a range of feasible business projects and, thus, is within the intrinsically entrepreneurial section of the population. He or she needs capital
to undertake a project. One possibility is to use own or family funds,
thereby making it unnecessary to borrow commercially. However, person
j may have lower savings than are required for the entrepreneurial venture.
Then there is no option but to try to obtain a business loan.
A banker in the above framework is likely to reason in the following
way. ‘‘I have little idea about whether project X will work out as Mr. A
says. I cannot assign it a probability. However, if Mr. A offers me collateral of Y, then I can make a loan of Y 0 d, where d is the cost of
reclaiming the collateral in the event of bankruptcy. This is effectively a
risk-free loan.’’ Thus secured (‘‘collateralized’’) loans are a rational re-
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sponse by bankers to imperfect knowledge. Such a view provides a natural
rationale for the existence of capital constraints.
Assume individual j can get an unsecured loan only z percent of the
time, where z is below unity. This is despite the fact that the business
venture is assumed sound. The reason for the apparent suboptimality is
that individual j has no way of assuring the typical banker that the hypothetical project is feasible. He may do so (perhaps because some within the
innovative entrepreneurial class become bankers), but not with certainty.
This approach makes genuine uncertainty a central feature of the analysis. By contrast, the recent work by Khilstrom and Laffont (1979),
Kanbur (1982), and Grossman (1984) breaks with the tenets of earlier
thought on entrepreneurial activity. Kanbur et al. develop a standard
neoclassical approach in which productive business opportunities are ex
ante feasible for, and visible to, all individuals (most simply choose not
to exploit them); there is an objective probability distribution governing
business risk, and everyone knows that distribution; entrepreneurs receive
the same expected utility as their workers; the entrepreneur is likely to
be someone with unusually low risk aversion (see especially Khilstrom
and Laffont 1979). These are different from the main assumptions and
arguments of classic sources such as Knight (1921), Schumpeter (1939),
and Kirzner (1973). In contrast to modern theory, the classic writings
about the nature of the entrepreneur stressed the following: most individuals are not sufficiently alert or innovative to perceive business opportunities; there is no objective probability distribution governing business risks;
an innovative entrepreneur may receive higher expected utility than he
or she would as a regular worker; attitude to risk is not the central
characteristic that determines who becomes an entrepreneur.
Our model draws upon the older, but recently neglected, current of
thought. It builds upon eight assumptions. Assume that proportion b of
the population has entrepreneurial vision. This group of individuals can
see business opportunities where proportion 1 0 b see none. There is, in
this economy, an array of potential entrepreneurial projects that have not
yet been developed, each of which requires a different amount of capital,
k. Each project needs one entrepreneur’s labor. The profit from project
k—indexing in this way for simplicity—is p(k). This function describes
the return from the different entrepreneurial ventures in the economy. It
is assumed to be strictly increasing, because any high-profit projects that
required little capital have already been undertaken. There is a distribution
of capital endowments across the population. Denote it f (k), where k
lies between zero and one. The latter normalizes the richest person’s
capital assets at unity.
An individual who perceives the array of business opportunities cannot
with certainty borrow the required capital unless he or she has access to
the necessary collateral. This is because, by their nature, such opportuni-
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ties are not within the vision of most other kinds of individuals (such as
bankers approached for loans). The individual can try to borrow for a
project but has only probability z of obtaining an unsecured loan.2 Denote
utility by u. Individuals receive utility u Å p / i in self-employment,
and u Å w in conventional employment, where w is defined as the wage
paid for nonentrepreneurial work, and i is the nonpecuniary utility from
being independent and one’s own boss. Assume that anyone can find
work at wage w in the nonentrepreneurial part of the economy. It is
assumed that w equals the marginal product of labor in that alternative
sector and that this is a declining function, w(N), of the number of
employees, N, in the sector. Population is normalized at unity. In equilibrium, the number of entrepreneurs is E.
These assumptions lead to a simple but fairly unconventional model.
To make the key points as simply as possible, all probabilistic business
risk has been assumed away. Many potential entrepreneurs are liquidityconstrained. People enter entrepreneurship until, in equilibrium, either
(i) capital or vision constraints are binding in aggregate or (ii) the utility
from running a business is driven down to equal to that from wage work.
In the latter case,
w Å p(k*) / i,
(1)
wage Å profit from self-employment
/ nonpecuniary utility from independence,
where k* is the amount of capital needed for the marginal entrepreneurial
project. All projects requiring more capital have here already been undertaken.
The number of entrepreneurs in the economy is
EÅb
*
1
f (k)dk / bz
k*
*
k*
f (k)dk
(2)
0
Å 1 0 N.
(3)
This is also, by the choice of units, the probability of self-employment
for one individual. From equation (2), the total number of entrepreneurs
in the economy is equal to the probability of ‘‘vision’’ multiplied by the
number of people with a greater capital endowment than k* (that needed
2
An interesting but complex project would be to construct a complete theory
of the determinants of z (the probability that someone with a good idea can
obtain a loan from bankers who cannot themselves perceive the business opportunity). This article requires only that z be less than unity.
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for the marginal project) plus the probability of vision multiplied by the
probability of successfully getting an unsecured business loan multiplied
by the number of individuals who are inherently short of capital.
Equilibrium in this economy can take two different forms. One is
described by the simultaneous solution of equations (1)–(3). This is the
case in which the market for entrepreneurs clears: the marginal entrepreneur earns utility (made up of profit plus the satisfaction from independence) equal to that from working in the wage sector. There is a second
possibility, and that is when there are insufficient entrepreneurs to drive
to zero the surplus from running the marginal business. When there is a
shortage of b-individuals with capital, those people earn a rent, so that
p(k*) / i ú w.
(4)
This distortion might be viewed as a result of the asymmetric information—about whether a project is good—between bankers and those individuals in the population who were born with entrepreneurial vision.
A number of results follow.
PROPOSITION 1. When the market for entrepreneurs clears ( p(k*)
/ i Å w), the following raise the equilibrium number of entrepreneurs
and the economy’s wage rate: (i) an increase in b, the proportion of the
population with (entrepreneurial) vision, (ii) a rise in i, the utility from
independence, (iii) an increase in z, the probability of loans to individuals
without sufficient capital.
PROPOSITION 2. When the market for entrepreneurs fails to clear
(p(k*) / i ú w), the following raise the equilibrium number of entrepreneurs and the economy’s wage rate: (i) an increase in b, the proportion
of the population with entrepreneurial vision, (ii) an increase in z, the
probability of loans to individuals without sufficient capital, (iii) a drop
in k*, the binding level of capital necessary to set up a business. Contrary
to the market clearing case, (iv) the utility from independence, i, has no
effect.
PROPOSITION 3. Entrepreneurs get higher utility than regular
workers.
PROPOSITION 4. When capital constraints bind, the larger is Z , the
number of people in the economy who have capital, the smaller is the
utility gap between entrepreneurs and workers.
For proofs, see appendix A.
The underlying idea is a simple one. At the individual level, there are
capital constraints. Some of the people with the ability to see good projects fail to obtain the funds to undertake them, because they do not have
a large enough capital endowment, k, or are not lucky enough to get an
unsecured loan. At the aggregate level, however, the capital constraint
may not bind. This is the case analyzed in proposition 1, where there is
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no distortion. The case in proposition 2 is different. Here the supply of
capital is so short that anyone who can raise the finance earns a form of
rent created by the asymmetric information in the economy.3 In equilibrium, either capital or vision constraints are binding in aggregate or the
utility from running a business has been driven down to equal that from
wage work. Entrepreneurs are better off than regular workers, and the
mean gap in utility between the two kinds of work is higher if there are
fewer numbers of people with capital.
This framework suggests two testable hypotheses. The first is the idea
that some potential entrepreneurs are constrained, by lack of access to
capital, to become employees rather than entrepreneurs. The second is
that individuals who run their own enterprises have higher utility than
those who are employees in the wage sector. Sections III and IV study
the first issue using an econometric test and complementary questionnaire
evidence. The second issue is intrinsically more difficult to assess, because
it requires data on utility levels in the two sectors. Following methods
more commonly found in psychology than economics, Section V implements a test using reported satisfaction levels as proxy utility data.
III. Data and Methods
Whether or not individual j is self-employed depends on a joint probability captured by the constituent parts of equation (2):
the probability of running a business Å (the probability of having
entrepreneurial vision)∗(the probability of having capital / the
probability of being able to get an unsecured loan given no capital).
Empirically these probabilities may be assumed to depend upon a set of
personal characteristics, especially measures correlated with the person’s
assets, and a set of regional and industrial characteristics. Rather than
work with a highly structured model, we estimate reduced-form equations based on a linearization of the assumed probability function and
uses standard personal variables plus a range of childhood variables.
Should the analysis focus upon transitions into self-employment or
upon cross-section evidence on those who are self-employed? Although
it would be useful to have results for pure transitions, there is a problem
with such an approach. Policy makers (as well as economists) are interested in entrepreneurs who are successful rather than unsuccessful, and
in small businesses that last rather than fail. Therefore, showing that
inheritances affect the flow into entrepreneurship would, in itself, be of
limited (though positive) value, for it could be that such individuals
3
It is assumed that the existence of any specialist venture-capital companies is
not sufficient to remove the distortion created by asymmetric information.
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quickly exit from self-employment. Establishing that a person’s access to
finance influences his or her decision to remain self-employed would,
similarly, also be of positive but limited interest, because such people
might be less likely to flow in to entrepreneurship in the first place.
A natural way to learn about the lasting influence of capital injections
such as inheritances is thus either (i) simultaneously to study both sets
of transitions (in and out) or (ii) to study the effects of earlier inheritances upon the cross-section probability of being self-employed. This
article—partly because of the nature of the data—adopts the second
approach. Recent work by Holtz-Eakin, Joulfaian, and Rosen (1994a,
1994b), which follows an early version of this article, takes route i and
shows that inheritances both raise entry and slow exit. Recent crosssectional Swedish evidence on ii is contained in Lindh and Ohlsson
(1996).
The econometric analysis described in the next section draws upon the
National Child Development Study (NCDS). This is a longitudinal birth
cohort study that takes as its subjects all those living in Great Britain who
were born between March 3 and 9, 1958. These children were surveyed at
birth and at ages 7, 11, 16, 23, and 33. At each of the first three followups, information was obtained from parents, teachers, and doctors. At
the most recent sweep, conducted in 1991 when all subjects were age 33,
information was also gathered about the respondent’s spouse and children. Details of the survey design are summarized in Elias and Blanchflower (1989).
We make use of information about employment status that was collected in the interviews of 1981 (NCDS4) and 1991 (NCDS5). These
have the useful feature that they provide snapshots of self-employment
activity when the individuals were in their early twenties and early thirties.
The 1981 sweep of NCDS contained 12,537 interviews. Of the total, 521
people were self-employed, while 8,657 worked as employees. Hence,
approximately one in 18 young people who were working at the time of
interview had a job which they had, in a sense, created themselves. The
1991 sweep contains data on 11,407 individuals. Of these, 1,279 were selfemployed, while a further 7,703 were employees. Thus, 10 years further
into the life cycle, the proportion of employment accounted for by the
self-employed had risen from 5.7% in 1981 to 14.2% in 1991. The period
itself probably accounts for some of this rise. In December 1981, there
were 21,142,000 employees in employment in Great Britain, of whom
2,093,000, or 9.9%, were self-employed. This compares with 21,506,000
employees in employment in December 1991, of whom 3,224,000, or
15%, were self-employed (Employment Gazette, vol. 93, no. 1 [May
1994]).
The empirical analysis focuses on individuals who were either employed or self-employed at the time of interview in either 1981 or 1991.
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In each year, we study cross-section patterns at that point in time. This
makes the nature of the equations different from Evans’s work with
Jovanovic and Leighton, where the data were on the flow into self-employment. We study the probability that an individual reports himself or
herself as self-employed. The dependent variable is therefore a stock
rather than a flow and so captures the combined effects of gifts and
inheritances (among other variables) on past movements into and out of
self-employment. However, some information is available on timing, and
the later results do more than look at simple cross-section correlations.
To produce plausible evidence that an access-to-capital variable influences entrepreneurial activity, it is necessary to have a well-designed statistical test. It is likely to be important to be able to argue that the capital
variable is exogenous or can be instrumented convincingly.
Two tests are done on 1981 data. One uses instrumental variables, the
other lags. The data set has the valuable feature that it records in 1981
(though not in 1991) whether the entrepreneur’s parents are alive or dead.
A variable for parental death then makes a natural instrumental variable
(in the NCDS data set, approximately 14% of individuals have at least
one parent who has died), because it should enter an inheritance equation
but not a self-employment equation. Unfortunately, this cannot be done
in the 1991 data, because parental death is not available in the later data.
In order to provide an additional test of the direction of causality, we
also use data on gifts/inheritances that were received many years before
the start-up decision.
The key question in the NCDS surveys is: ‘‘Have you (or your husband/wife/partner) ever inherited, or received as a gift from another
person, money, property, or other goods to the value of £500 or more?’’
(NCDS4, question 9; NCDS5, question E11). This question was asked
in both sweeps of the National Child Development Study. In 1981, 1,060
working individuals responded positively to this question. These respondents were asked to report both the amount of the gift/inheritance and
the date of its receipt. Of these monies, 6.4% were received before 1975,
25.7% between 1975 and 1978, and the rest received between 1979 and
1981. In the 1991 data, 2,927 working individuals said they or their
spouses had received a gift or inheritance of £500 or more. Eighty percent
of these inheritances or gifts had been received since 1981.
For analysis, these data on inheritance/gift payments were converted
into constant 1981 pounds sterling by compounding the UK Treasury
Bill interest rates from 1958 to 1991. Among those who received a sum,
the mean size of payment received by workers was £3,617 in 1981 (with
a standard deviation of £8,421) and £5,655 in 1991 (with a standard
deviation of £18,700). Only the largest inheritance/gift was recorded, so
it is not possible to aggregate over any multiple gifts. It was thought best,
for later analysis, not to exclude gifts received by married people’s spouses
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Table 1
Size of Inheritances/Gifts
Size of Inheritance/Gift
A. NCDS4, individuals aged 23 years:
£0
£500–£999
£1,000–£1,999
£2,000–£4,999
£5,000–£9,999
£10,000–£19,999
£20,000 and over
B. NCDS5, individuals aged 33 years:
£0
ú£0–£499
£500–£999
£1,000–£1,999
£2,000–£4,999
£5,000–£9,999
£10,000–£19,999
£20,000–£49,999
£50,000 and over
% of
Employed
% of SelfEmployed
% of Each
Category
Self-Employed
N
88.7
3.5
3.9
2.3
.8
.5
.3
83.8
2.7
4.5
3.3
2.9
1.8
1.0
5.3
4.4
6.4
7.8
18.1
18.4
16.1
8,089
319
361
219
83
49
29
72.6
5.7
5.9
5.2
4.9
2.8
1.6
1.0
.4
71.0
4.3
4.8
4.5
6.2
3.1
2.8
2.3
1.0
14.0
11.1
11.9
12.5
17.3
15.7
22.4
27.5
33.3
8,159
578
600
527
530
306
203
138
45
NOTE.—All values here are in constant 1981 pounds.
before the marriage took place (because those spouses could have later
used the money in their partner’s business).
The distribution of inheritances or gifts in constant 1981 pounds is
reported in table 1 from both NCDS4 and NCDS5. These raw data reveal
a strong positive relationship between the size of inheritances/gifts and
the incidence of self-employment. The first two columns of the distribution give the proportion of the employed and the self-employed who
received an inheritance and/or a gift. The third column reports the proportion of individuals who were self-employed.
IV. Self-Employment Probits Using NCDS Data
Tables 2 and 3 estimate self-employment probit equations using
NCDS4, that is, the data when the respondents were 23 years old. The
independent variables include standard personal characteristics, regional
variables, information on the father’s occupation when the respondent
was 14 years old, and three variables derived from personality reports
from a school teacher when the respondent was seven years of age. These
can be viewed as approximately predetermined variables. The variables
for father’s occupation are more detailed than is common in microeconomic data sets. Usefully, they make it possible to control for whether
the individual’s father was a farmer or self-employed. The full definition
of ‘‘own account’’ is not given in the codebooks but appears to be an old-
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Table 2
Probit Equations for Self-Employment at Age 23 in 1981
Nonlinear
Two-Stage
Least Squares
(4)
(1)
(2)
(3)
...
...
...
Log of inheritance/gift*
...
.00016
(.00004)
0.0041
(.0019)
...
...
Inheritance/gift squared∗106
.00002
(.00001)
...
Inheritance/gift
Unforthcoming score
Hostility score
Acceptance anxiety score
Apprenticeship
Father: manager employing õ25
Father: own account worker
Father: farmer employer
Father: farmer own account
Father: agricultural worker
Female
Log county unemployment rate
Constant
Log likelihood
x2
Pseudo R2
Restricted log likelihood
N
0.0491
(.0266)
.0563
(.0352)
0.0659
(.0742)
.4895
(.1245)
.6077
(.1470)
.7227
(.2366)
2.0003
(.2800)
2.3013
(.2833)
.5743
(.4051)
01.0413
(.1359)
0.9408
(.1924)
0.5540
(.4571)
01,368.03
238.22
.0801
...
6,885
0.0487
(.0266)
.0556
(.0353)
0.0663
(.0744)
.4997
(.1249)
.5693
(.1480)
.7053
(.2370)
1.9520
(.2813)
2.3046
(.2838)
.6085
(.4052)
01.0607
(.1367)
0.9063
(.1928)
0.6725
(.4589)
01,359.83
254.62
.0856
...
6,885
.0406
(.0120)
0.0475
(.0266)
.0557
(.0353)
0.0676
(.0740)
.4913
(.1247)
.5725
(.1477)
.7100
(.2369)
1.9823
(.2785)
2.2878
(.2845)
.6078
(.4051)
01.0580
(.1363)
0.9293
(.1923)
0.4438
(.469)
.1994
(.0672)
0.0380
(.0330)
.0599
(.0375)
0.0801
(.0771)
.5725
(.1434)
.3857
(.1785)
.6948
(.2328)
1.9098
(.2093)
2.5238
(.3103)
.9587
(.3103)
01.2976
(.2668)
0.8689
(.2330)
0.3522
(.5347)
01,364.25
...
245.78
...
.0826
...
...
0347.98
6,885
6,885
SOURCE.—National Child Development Study, 1981.
NOTE.—Standard errors are in parentheses. The variables for unforthcoming, hostility, and acceptance
anxiety are from psychological assessments made during childhood. ‘‘Own account’’ means a form of
self-employment. Columns 1, 2, and 3 have no instrumenting. Column 4 instruments the inheritance
variable with variables for mother dead, father dead, both parents dead, county unemployment rate,
gender, and five dummies for father’s social and occupational class.
* Zero inheritances were set to .01 before taking natural logarithms.
fashioned term for being self-employed without employees. By contrast, a
‘‘farmer employer’’ has employees. Experimentation with a further set of
possibly endogenous variables, such as marital status and educational
qualifications, left the key results unaltered. Although they are not the
focus of the article, it is worth noting the negative effects of the local
unemployment rate and the female dummy (replicating findings on a
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38
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Table 3
Self-Employment Probit Equations Controlling for the Nature of Firm and
Inheritance: Age 23 in 1981
Not Family
Firm
(1)
Log of inheritance/gift*
Unforthcoming score
Hostility score
Acceptance anxiety score
Apprenticeship
Father: manager employing õ25
Father: own account worker
Father: farmer employer
Father: farmer own account
Father: agriculture worker
Female
Log county unemployment rate
Constant
Log likelihood
x2
Pseudo R2
N
Inheritance/Gift
before 1978
(2)
.0359
(.0140)
0.0423
(.0298)
.0817
(.0369)
0.0489
(.0809)
.5226
(.1400)
.2876
(.1763)
.5472
(.2720)
.7627
(.4790)
.6185
(.6066)
.5840
(.4345)
0.9830
(.1547)
01.0809
(.2172)
0.3204
(.5126)
01,127.84
127.88
.0537
6,786
.0655
(.0176)
0.0525
(.0279)
.0746
(.0358)
0.1096
(.0806)
.4323
(.1321)
.5997
(.1562)
.6605
(.2544)
1.9209
(.3089)
2.3288
(.2994)
.6559
(.4066)
01.0808
(.1439)
01.1061
(.2042)
.1170
(.4865)
01,230.37
228.06
.0848
6,321
Inheritance/Gift
¢3 Years Prior
Self-Employment
(3)
.0707
(.0272)
0.0646
(.0291)
.0787
(.0365)
0.1074
(.0816)
01.1039
(.2095)
.6238
(.1606)
.6887
(.2607)
1.9300
(.3188)
2.5002
(.3040)
.6872
(.4075)
01.0688
(.1484)
01.1039
(.2095)
.1154
(.5061)
01,175.35
223.49
.0868
6,165
SOURCE.—National Child Development Study, 1981.
NOTE.—Standard errors are in parentheses. These equations are not instrumented. Column 1 is for
the subsample of self-employed people who did not work in family firms. Column 2’s inheritance
variable is for gifts and inheritances received prior to 1978. Column 3’s inheritance variable is for gifts
and inheritances received at least 3 years before the person entered self-employment.
* Zero inheritances were set to .1 before taking natural logarithms.
different UK data set in Blanchflower and Oswald [1990a]), the significant effects of father’s occupation, and the borderline influence of childhood psychological traits. On the latter issue, many other psychological
variables were tried unsuccessfully and hence were omitted.
As a test of the liquidity constraint hypothesis, the equation includes,
sometimes as a quadratic function, a variable for the value of any inheritance or gift. The variables ‘‘Inheritance/gift’’ and ‘‘Inheritance/gift
squared’’ denote the level and square of the size of the largest amount
received by the individual.
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What Makes an Entrepreneur?
39
Column 1 of table 2 reveals that, in the simplest linear specification,
the inheritance/gift variable enters positively with a coefficient that is
positive and statistically significant at the 5% level. Column 2 tests for a
quadratic form. Both the level and the square are significant at better than
the 5% level. Further evidence for a nonlinearity emerges from column
3 of table 2, which switches to the logarithm of inheritance/gift (assigning
0.01 to those values of zero). The coefficient enters with a coefficient of
0.04 with a standard error of 0.01. For simplicity, this article continues
to use a log structure in table 3.
Table 3 probes the nature of the correlation between self-employment
and receipt of an inheritance or gift. First, in column 1 of table 3 the sample
is restricted to those who are not self-employed in family firms (using
answers to the question ‘‘Are you self-employed in a firm belonging to your
family?’’). The gift/inheritance variable remains strongly significant. The
column 1 estimation is an attempt to demonstrate that table 2’s inheritance/
gift effect is not merely proxying for the fact that children inherit family
firms. It is possible, as a referee has pointed out, that some people who
inherited businesses from their parents may not refer to them as family
firms, but the variable is the best available in this data set. Second, because
children who are about to go into business may approach their own families
for loans, it is possible that the results in columns 1–3 of table 2 might be
contaminated by simultaneity bias. Although the need to use family money
in this way could itself be construed as an illustration of capital constraints,
two procedures were followed in an attempt to allow for the possible endogeneity of the inheritance/gift variable.
Column 4 of table 2 reports estimates using nonlinear two-stage least
squares. Here a good instrument is required, and the data set seems to
contain one. The variable for inheritances or gifts is assumed to be a
function of whether one or both of the individual’s parents had died
(research on the determinants of inheritance is sparse but includes Cox
[1987] and Wilhelm [1991]). This ought to be an effective way to instrument, because the death of a father or mother should have no effect on
the self-employment decision per se but should, and does, enter significantly into an equation for inheritances or gifts (examples of such equations are given in app. A). Approximately 14% of the sample had lost
at least one parent in 1981. The exact figures are in table A.
To exploit a form of lag as an alternative to instrumenting, columns 2
Table A
Father alive
Father dead
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Mother Alive
Mother Dead
10,797
1,135
426
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40
Blanchflower/Oswald
and 3 of table 3 use only data on those inheritances and gifts that came
well before the self-employment decision. This approach attempts to
establish causality, and so solve the potential simultaneity problem, by
using a predetermined inheritance/gift variable. The coefficient in each
case is approximately 0.07 with a standard error of less than 0.03, so this
method seems to confirm the role of inheritances/gifts in entrepreneurship equations and to suggest that the evidence for effects from capital is
not created by simultaneity bias. A 3-year preinheritance interval was
chosen as a compromise between the need for as long a lag as possible
and the requirement that the number of observations not be too few.
The findings from these different sets of results—uninstrumented, instrumented, and lagged—all find statistically significant inheritance effects and are thus consistent with the existence of capital constraints. The
size of the inheritance/gift effects is large. From the probit equations it
is possible to calculate that individuals who had received £5,000 ($9,000)
in constant 1981 pounds sterling were approximately twice as likely to
be self-employed in 1981 as those who had received nothing (the instrumental variable [IV] estimates are even greater, and perhaps implausibly
large). For example, a male in the southeast of England, with an apprenticeship and whose father was a manager in a workplace with under 25
employees, had a probability of 0.16 of being self-employed without an
inheritance and/or gift. This probability was 0.37 if he had received an
inheritance of £5,000. In the case of females, the probabilities were 0.07
and 0.21, respectively. This seems a big effect even when contrasted with
the Evans-Jovanovic estimate that removing all liquidity constraints
would increase the flow of entrepreneurs from 3.8% to 5.1% (Evans and
Jovanovic 1989, p. 824). The likely explanation—apart from possible
U.S. and UK differences and the need for caution in interpreting all
estimates in early work in a field—is that capital constraints bind more
on the young.
It might be argued that age 23 is too young to study self-employed
people. Hence, table 4 re-does the analysis for the 1991 sweep of the
panel, that is, when these individuals were age 33. Table 4 provides a
probit equation for self-employment at that date. It is designed to be close
in specification to the earlier tables and continues to find an apparently
powerful correlation between self-employment status and having received
an inheritance or gift. The sample consists of individuals in employment
at the time of interview in 1991 when the respondents were 33 years of
age. As in tables 2 and 3, the dependent variable is set to one if selfemployed in the main activity and to zero otherwise. The self-employed
were only slightly more likely to have received an inheritance than the
employed (29% compared with 27.4%, respectively), but the amount
received was much higher (the mean level of inheritance received was
£4,692 for the employed and £11,148 for the self-employed).
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Table 4
Probit Equations for Self-Employment at Age 33 in 1991
/ 9e0e$$ja14
Inheritance/gift ∗ 10
2
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
.00085
(4.88)
...
.00058
(3.17)
...
.00072
(2.42)
0.00061
(.63)
...
.00073
(4.29)
...
.00098
(3.74)
0.00118
(1.32)
...
.00068
(3.97)
...
.00071
(4.15)
...
.00051
(2.79)
...
.00060
(3.18)
...
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Self-employed in 1981
...
.0012
(4.65)
0.0017
(1.98)
...
Unforthcoming score
...
...
...
...
...
Hostility score
...
...
...
...
...
Acceptance anxiety score
...
...
...
...
...
...
...
...
Father: manager employing õ25
...
...
Father: own account worker
...
...
Father: farmer employer
...
...
Father: farmer own account
...
...
Father: agricultural worker
...
...
Father: class missing NCDS2
...
...
.2784
(2.72)
.4372
(3.44)
.8389
(5.12)
.6887
(3.79)
.1975
(1.15)
...
.2798
(2.73)
.4389
(3.46)
.8360
(5.10)
.6910
(3.80)
.2015
(1.17)
...
0.3596
(10.30)
...
0.3596
(10.29)
...
0.3412
(8.87)
...
0.3412
(8.87)
...
.2800
(2.73)
.4395
(3.46)
.8338
(5.09)
.6926
(3.80)
.2031
(1.18)
.1024
(1.05)
0.3584
(10.19)
...
.2826
(2.76)
.4425
(3.48)
.8294
(5.06)
.6964
(3.82)
.2098
(1.21)
.1060
(1.08)
0.3584
(10.19)
...
.2681
(2.60)
.4281
(3.35)
.8668
(5.23)
.7050
(3.81)
.2460
(1.41)
.1051
(1.07)
0.3576
(10.08)
...
...
...
0.5877
(11.66)
...
...
0.5921
(11.73)
...
12
0.7052
(6.89)
...
12
0.7095
(6.91)
...
12
0.6877
(6.86)
...
12
0.6949
(6.91)
10
12
0.9037
(6.99)
.2628
(2.56)
.4329
(3.40)
.8444
(5.13)
.7018
(3.84)
.1891
(1.09)
.0980
(1.00)
0.3579
(10.13)
0.4611
(3.78)
...
12
.2654
(.98)
Inheritance/gift squared ∗ 10
8
Female
Log regional unemployment rate
UC: Labor Econ
Regional dummies
Parental social class dummies
Constant
Log likelihood
x2
N
03,511.82
137.61
8,757
03,510.36
140.51
8,757
...
02,900.20
187.40
7,322
02,900.02
187.76
7,322
...
03,468.41
223.82
8,755
...
...
...
...
...
...
...
...
...
0.0110
(1.33)
.0146
(1.17)
0.0064
(2.25)
.2808
(2.61)
.4387
(3.29)
.9257
(5.44)
.7547
(3.97)
.2938
(1.64)
.0814
(.77)
0.3611
(9.47)
...
03,467.71
225.21
8,755
03,419.20
279.66
8,710
03,440.30
237.46
8,710
10
12
0.8597
(6.32)
03,030.03
259.74
7,760
1.4757
(19.91)
...
...
...
.2320
(1.81)
.4432
(2.82)
.6958
(3.18)
.5623
(2.45)
0.0526
(.22)
.0515
(.41)
0.2941
(6.42)
...
10
12
01.2191
(7.14)
02,162.18
649.36
5,998
42
Blanchflower/Oswald
Column 1 of table 4 is a probit equation that includes only the amount
of inheritance/gift in constant 1981 pounds. The level of inheritance/gift
enters with a coefficient of 0.85*10 05 and a t-statistic of nearly 5. This
is not a small effect (given the amount of variation in the data). In column
2, there is only marginal evidence that the relationship is a quadratic.
A log specification also worked reasonably well (not reported). The
inheritance effect is robust to the inclusion of parental class variables in
column 3. These variables work very similarly to those in tables 2 and 3
above. The squared inheritance term is insignificant when parental social
class—proxied by 12 dummies for father’s occupation—is controlled for
in column 4. It never achieves significance once other controls are included; hence in subsequent specifications in this table it is omitted. To
check for the possibility of attrition bias, column 5 and onward uses a
bigger sample: it includes all those cases that had missing values to the
parental class variable. We set all of the other social class dummies to
zero for such cases and include a further dummy variable, ‘‘Father: social
class missing NCDS2.’’ This is everywhere insignificant, and the other
coefficients are essentially unchanged, which may suggest that these results are not biased by attrition. The exact measures of father’s occupation
are listed in the regression in appendix E.
The inheritance variable is robust to the inclusion of regional dummies
(col. 7), or the regional unemployment rate (col. 8), which once again
enters negatively. Column 9 of table 4 includes the three personality
scores provided by the schoolteacher when the respondent was aged
seven. Those anxious for acceptance as children are less likely, at age 33,
to be self-employed. Finally, in column 10, a form of lagged dependent
variable is included. It records self-employment status in the earlier sweep
of the NCDS survey, namely, in 1981. Even controlling for this, inheritance continues to be significant and to be of approximately the same
size. This article does not re-do table 3 for the later sample of 33year-olds. One reason is that NCDS5 does not record information on
family firms.
As usual with econometric analysis, we cannot rule out the possibility
that the observed empirical correlation is due to some other effect. Perhaps wealth makes people less risk-averse and thus more prone to go into
business, or self-employment allows wealthier individuals to consume
leisure more easily. Yet the apparently sizable effects from small inheritances do not make such an interpretation look the most natural one. As
the next section shows, moreover, there is other evidence.
At a referee’s suggestion, some final checks were performed. First, table
2’s regressions were re-done using only the subsample of individuals who
did not receive a gift or inheritance, but including dummy variables for
whether one or both parents of the individual had died. This tests whether
parents’ death might directly cause independence or an entrepreneurinal
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What Makes an Entrepreneur?
43
Table 5
How Seriously Have You Considered Becoming Self-Employed? (%)
1983
1984
1986
1987
1989
All
Very
Seriously
Quite
Seriously
Not Very
Seriously
Not at All
Seriously
N
5.3
6.6
6.1
4.9
5.9
5.7
11.9
10.3
9.5
9.7
9.9
10.1
12.6
12.3
14.2
14.0
11.8
13.0
70.2
70.7
69.9
71.4
72.5
71.1
779
724
1,470
1,273
1,691
5,932
SOURCE.—British Social Attitudes Surveys (weighted). Own calculations.
NOTE.—Base: all individuals who were employees when interviewed and who had never been selfemployed in the preceding 5 years.
spirit. However, reassuringly for this article, the parental-death dummy
variables were grossly insignificant. Second, for a subsample of individuals
who had had no inheritance or gift prior to 1981, a probit equation was
estimated to see if those who had a gift/inheritance between 1981 and
1991 were more likely to have have been self-employed in 1981. This
tests the idea that any crucial omitted variables—linking self-employment
and inheritances through other, possibly unknown, mechanisms—would
probably begin to have an effect before the gift/inheritance was received.
Supportively for our conclusions, however, the variable for being selfemployed in 1981 entered with a t-statistic of only 0.1. These two checks
seem to be consistent with our favored approach.
V. Interview Evidence on Capital Constraints
New survey findings are consistent with the idea that both current
and potential self-employed business owners feel constrained by limited
capital. Our aim in this section is to provide evidence more direct than,
and complementary to, that reported in the previous section. Two
previously unexploited sources of information are used. The first is
the British Social Attitudes (BSA) Survey series, an annual random sample providing data from 1983 to 1989. The second is a 1987 government-sponsored random survey, the National Survey of the SelfEmployed (NSS).
The BSA survey asked 5,947 randomly chosen employees who had not
been self-employed in the previous 5 years (97.1% of all employees) the
question, ‘‘How seriously have you considered being self-employed?’’
The answers are given in table 5. On average, 16.8% had considered
running their own business either ‘‘very seriously’’ or ‘‘quite seriously.’’
In 1983, 1984, and 1986 a subsample of 451 respondents who had considered it very or quite seriously were asked the follow-up question, ‘‘Why
did you not become self-employed?’’ The answers, which were recorded
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Blanchflower/Oswald
Table 6
What Was the Reason You Did Not Become Self-Employed? (%)
Year
Lack of
Capital/Money
Risk
Economic
Climate
Other
Reasons
N
1983
1984
1986
All
59.3
56.0
44.7
51.3
10.2
12.1
22.1
10.6
2.5
.9
.9
1.3
28.0
31.0
32.3
31.2
118
116
217
451
SOURCE.—British Social Attitudes Survey Series (weighted). Own calculations.
NOTE.—Base: employees who reported that they had considered becoming self-employed ‘‘very
seriously’’ or ‘‘quite seriously’’ in table 4.
in their own words, are reported in table 6. The table reveals that, aggregating over the years, approximately half the group gave as their reason
for not setting up in business that they could not obtain the necessary
capital. It was the most common reason. This is one form of evidence on
the relevance of binding liquidity constraints.
The National Survey of the Self-Employed, which apparently has not
been used before by economists, draws on information from a random
sample of approximately 12,000 adults interviewed in Britain in the spring
of 1987. Individuals who were recently self-employed were asked to name
the main source of finance used to set up their business. Out of the 243
respondents who were in this special category, 4 103, or 42%, reported
that they used their own savings to set up the business, 36, or 15%, used
money from family or friends, while only 41, or 17%, took a bank loan.
Taking this group as the base, table 7 provides the answers to the question,
‘‘What help would have been most useful to you in setting up your
business?’’ It reveals that assistance with money and finance was the most
commonly recorded item (mentioned by a quarter of respondents), which
is again consistent with the capital-constraint hypothesis.
In addition to interviewing the self-employed, the NSS also obtained
information on 139 individuals who said they were ‘‘seriously intending’’
to become self-employed. They were asked the following: ‘‘There are
many anxieties and concerns people have in setting-up in self-employment. What are you most concerned about?’’ They were then given a list
of 20 possible answers from which they had to select one. The main
responses are reported in table 8 and show that the single most common
answer was that individuals were worried about how to raise the necessary
finance (mentioned by a fifth of respondents).
4
Data sets covering newly self-employed entrepreneurs are almost inevitably
small, so the best that can be done is to insist that data are drawn from a welldesigned random sample.
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What Makes an Entrepreneur?
45
Table 7
What Help Would Have Been
Most Useful to You in
Setting-Up in Business? (%)
Money/finance
How to start up
Government regulations
Tax advice
Bookkeeping
Legal advice
Finding premises
Finding clients
Marketing/advertising
General advice
Others
No help desired
No. of observations
26.3
7.8
.8
9.1
4.1
1.2
2.5
3.7
1.6
5.3
5.1
32.5
243
SOURCE.—1987 National Survey of the
Self-Employed. Own calculations.
NOTE.—Base: adults who had become
self-employed in the previous 4 years, were
still self-employed and had fewer than six
employees.
These two questionnaire surveys provide information about the problems that potential and current self-employed people think are most important. In each case, the dominant answer concerns the availability of
capital. Although economists are schooled to be cautious of survey inforTable 8
What Was Your Biggest Concern
with Becoming Self-Employed? (%)
Where to get finance
Cash flow
How to start
Where to get advice
Finding premises
Finding clients
Competition
No guaranteed income
Losing savings
Understanding tax
Understanding bookkeeping
Pension
Employing people
Effect on family
Others
No concerns
No. of observations
20.1
10.8
4.3
5.0
5.0
10.1
3.6
14.4
2.9
14.4
3.6
2.9
2.2
4.3
8.0
3.6
139
SOURCE.—1987 National Survey of the SelfEmployed. Own calculations.
NOTE.—Base: those ‘‘seriously intending’’ to
become self-employed in the next few months.
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Blanchflower/Oswald
Table 9
Overall Satisfaction with Job: Age 23 in 1981
% Answering
Very dissatisfied
Dissatisfied
Neither
Satisfied
Very satisfied
N
Self-Employed
Employees
All Workers
1.7
2.9
6.7
42.4
46.2
519
2.8
9.6
8.2
50.2
29.1
8,657
2.8
9.2
8.1
49.8
30.1
9,176
SOURCE.—National Child Development Study, 1981.
NOTE.—Base: individuals in employment at the time of interview.
mation, it seems unlikely that there is nothing to be learned from this
common message from different surveys. They appear to sit consistently
alongside the estimation results.
VI. Testing Whether the Self-Employed Are Happier
The model implies (proposition 3) that those running their own enterprises will be happier than employees. For a test of this, it is necessary
to compare the total returns to conventional work and entrepreneurial
activity. The reported earnings of self-employed individuals are known
to be unreliable, and it is likely, as the model suggests, that such individuals get a nonpecuniary benefit from being their own boss. Hence, some
proxy for overall utility is required. We follow the psychology literature
in using survey data on job and life satisfaction. It is established there
(see, e.g., Warr 1985 and Argyle 1989) that reported satisfaction numbers
are highly correlated with observable measures of individual well-being
at work such as quitting behavior and physiological symptoms. The small
economics literature includes Hamermesh (1977), Freeman (1978), Borjas (1979), Schwochau (1987), Meng (1990), Miller (1990), Clark and
Oswald (1994, 1996), and Blanchflower and Freeman (1994).
The central issue is whether, ceteris paribus, the self-employed report
higher levels of overall utility or job satisfaction than do employees. The
National Child Development Study is again a valuable data source. After
asking each 23-year-old individual how satisfied they were with a range
of items, such as pay and working conditions, the following encompassing
question was asked: ‘‘Taking everything into consideration, how satisfied
or dissatisfied are you with your job as a whole?’’ (NCDS4 questionnaire,
question 19, p. 9). Preliminary questions were asked about individual
components of utility. Respondents presumably saw this question as requesting information on their entire ‘‘utility package,’’ and this makes
the answers potentially useful.
The responses to the job satisfaction question are coded in the survey
into five categories. A cross-tabulation of the results is reported in table
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What Makes an Entrepreneur?
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9. Insofar as the responses can be seen as a genuine proxy for utility
levels, they appear to favor the view that the self-employed are ‘‘happier’’
at work. The table shows that 46% of the self-employed say that they
are in the top category of very satisfied, whereas the figure is 29% for
employees.
To control for other characteristics, ordered probit equations are estimated in table 10. Because satisfaction is presumably influenced by income, some stance must be taken on whether or not an earnings measure
is to be included in the probit equations. The theory makes clear that the
appropriate test is to omit earnings variables. This is because the focus
of interest is the total utility of individuals—to allow a comparison of
employment versus self-employment—and not just the satisfaction level
after income is held constant.
Included as controls in the table 10 equations are dummy or continuous
variables for self-employment, gender, disabled status, union membership, marital status, region, highest educational qualification, part-time,
ever unemployed in the previous 5 years, a dummy for problems with
Table 10
Ordered Probit on Overall Satisfaction with Job: Age 23 in 1981
All
Variable
No Inheritance
Inheritance
Coefficient t-Ratio Coefficient t-Ratio Coefficient t-Ratio
Self-employed
Female
Disabled
Number problems
Married
Divorced
Separated
Part-time
Union member
Ever unemployed
Completed
apprenticeship
Experience (months)
Tenure in current job
(months)
Constant
Threshold 1
Threshold 2
Threshold 3
Log likelihood
Restricted log
likelihood
x2 (49)
N
.4235
.1156
0.1062
0.1442
.0694
0.0468
.0778
.1290
0.0484
0.1938
4.930
3.958
.061
2.483
2.580
.367
.906
2.095
1.744
7.076
.4911
.1311
0.0034
0.1449
.0786
0.0831
.1085
.0948
0.0498
0.1777
5.278
4.189
.019
2.380
2.711
.635
1.178
1.445
1.681
6.073
0.0266
.0417
0.2521
0.1663
0.0074
.6753
0.4742
.4536
.0177
0.2959
.106
.470
.165
.795
.092
.942
1.808
2.382
.204
3.563
0.0236
.0008
.556
.776
0.0143
.0008
.316
1.501
0.0904
0.0032
.673
1.207
0.0001
2.1924
.7748
1.1207
2.5344
.016
15.007
28.343
38.541
78.574
0.0001
2.1152
.7709
1.1160
2.5393
.262
13.541
26.437
35.929
73.538
0.0007
2.5307
.8324
1.1971
2.5892
.425
5.281
9.272
12.656
25.303
09,536.3
08,323.8
01,184.9
09,717.9
363.27
7,874
08,497.0
346.35
6,887
01,219.9
70.048
987
SOURCE.—National Child Development Study, 1981.
NOTE.—Ten region dummies, 4 month-of-interview dummies, 12 highest-qualification dummies, and
9 industry dummies are also included.
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Blanchflower/Oswald
arithmetic, months of experience, and job tenure. Month-of-interview
dummies are included. A set of industry dummies are also included in
table 10. It is apparent that the self-employment dummy variable is significant. It was so in all specifications, including those with few control
variables. Consistent with the cross-tabulations presented in table 9, selfemployment has a positive effect on reported satisfaction levels (one that
is quantitatively large). As the equations exclude income measures, the
self-employment variables are not capturing merely the nonmonetary
return to being one’s own boss but, rather, a mixture of money and other
things. Given the paucity of work with data like these, the other controls
may also be of interest. Women are more satisfied; married people are
also more satisfied. Those who have been unemployed are less satisfied.
Union members are marginally less content: this replicates the main finding of the earlier literature of Freeman (1978) and Borjas (1979). Low
qualifications (in results not reported) and part-time work are also positively associated with satisfaction—perhaps reflecting the low-aspiration
effects discussed in Clark and Oswald (1996).
As an experiment into the effects of access to capital, the data were
split into two subsamples. The second set of columns of table 10 is
estimated with data on the 6,887 people who reported themselves as
having received no inheritance or gift of money or goods exceeding £500.
The third set of columns of table 10 gives estimates for the subsample of
987 people who had received this kind of inheritance or gift. There is
evidence that the self-employment dummy variable has little effect in the
group who inherited; the dummy even goes negative. Such evidence might
be taken to be consistent with the idea that those with capital—through
an inheritance—are more able to enter the self-employment sector and
drive down the rents available there. This argument can only be suggestive
but indicates an area where further research might be fruitful.
Table 11 presents related results for the 1991 data. Here there is no
question asked about individuals’ satisfaction with their work, so instead
the dependent variable is the answers to a question about life satisfaction.
The question asked was as follows: ‘‘Here is a scale from 0 to 10. On it,
‘0’ means that you are completely dissatisfied and ‘10’ that you are completely satisfied. Please ring one number to show just how dissatisfied or
satisfied you are about the way your life has turned out so far’’ (NCDS5
questionnaire, sec. I, question 8, p. 19). In the life satisfaction equation,
a self-employment dummy enters positively.5 Females and married people
5
We have replicated this positive self-employment result in happiness equations
for eight other Western countries (Great Britain, Northern Ireland, United States,
Italy, Eire, Israel, Norway, West Germany, and New Zealand) using ISSP data
for 1991 and for the United States using a time series of cross sections from
General Social Surveys, 1972–90 (see app. B). Appendix tables D1 and D2 report
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Table 11
Life-Satisfaction Equation: Age 33 in 1991
(1)
Self-employed
Female
Ever married
.1101
(2.16)
.0861
(2.43)
...
Union member
...
Race dummies
Qualification
dummies
Region dummies
Health dummies
Constant
...
RV 2
F
N
...
...
...
7.3385
(133.12)
(2)
(3)
.1015
(2.00)
.1049
(3.05)
1.1187
(25.62)
.0995
(2.74)
...
.1091
(2.15)
.1096
(3.18)
1.1180
(25.52)
.1085
(2.98)
7
...
...
...
6.3630
(94.97)
...
...
...
6.3624
(94.65)
(4)
(5)
.1114
(2.18)
.0971
(2.74)
1.1132
(25.26)
.0911
(2.48)
7
11
...
...
6.3096
(71.87)
(6)
.1089
(2.19)
.0961
(2.71)
1.0989
(24.81)
.0816
(2.20)
7
11
10
...
6.3943
(56.11)
.1068
(2.11)
.1026
(2.95)
1.0998
(25.30)
.0929
(2.55)
7
11
10
4
6.8966
(59.84)
.0009
.0739
.0745
.0773
.0790
.1240
4.78
168.16
61.85
32.04
22.75
32.40
8,442
8,385
8,318
8,153
8,113
8,046
SOURCE.—National Child Development Study, 1991.
NOTE.—t-statistics are in parentheses. The dependent variable is ‘‘satisfaction with the way life has
turned out.’’ It is scored from a minimum of zero to a maximum of ten. This is an OLS regression.
Means of life satisfaction: self-employed 7.561 and employees 7.464.
are significantly more satisfied. The union dummy here enters positively,
suggesting that such people are happier even if (as in the earlier table 10)
they may be less satisfied with their job. It is difficult to know what to
make of this difference. Columns 1–6 build up to a specification including
personal and regional variables. The finding that the self-employed are
happier appears to be robust.
These results provide some evidence that entrepreneurs get higher utility than conventional employees. One caveat should be borne in mind
when interpreting this study’s findings. The use of satisfaction and happiness data to proxy utility levels is unconventional in economics research.
It may be that reported satisfaction levels are subject to important biases.
For example, self-employed people may be intrinsically more optimistic
and cheerful than others, or may feel psychologically compelled, because
their business is in their own hands, to answer in the way they do.
Nevertheless, at this juncture a more straightforward interpretation of
the data is that the self-employed really are happier.
the exact questions asked, the distribution of responses, and ordered probits
equations for happiness. In both cases, self-employment has a significant positive
effect. Clark and Oswald (1994), using a medical measure of psychiatric health,
uncover a somewhat different result, namely, that the self-employed are more
highly stressed than are employees.
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VII. Conclusions
The forces that affect the supply of entrepreneurship are widely viewed
as important but poorly understood. We use survey and microeconometric methods to study a simple class of entrepreneurs, namely, individuals who run their own businesses. We draw upon data from the National
Child Development Study, the British Social Attitude Surveys, the International Social Survey Programme, the U.S. General Social Surveys, and
the National Survey of the Self-Employed.
The empirical results are consistent with the hypothesis that entrepreneurs face finance and liquidity constraints. In an ideal world, this would
be studied by constructing a laboratory or field experiment. In such an
experiment, the behavior of a group of individuals who are randomly
given capital would be compared with the behavior of people in a control
group who are given nothing. Such an approach is probably not feasible
in a subject like economics. However, a natural experiment in the same
spirit is generated by the fact that some individuals receive inheritances
and gifts.
In the first part of the article, a theoretical model was constructed in
which capital-constrained individuals choose between employment and
self-employment. The main idea is a simple one. It is that entrepreneurial
projects are, by their nature, difficult for bankers to assess probabilistically. Hence, those bankers are likely to require collateral, and that in
turn may hold back potential entrepreneurs. Consistent with this, the
empirical analysis produces four main conclusions.
1. The receipt of an inheritance or gift seems to increase a typical
individual’s probability, ceteris paribus, of being self-employed. This
emerges from NCDS data. It is not an estimate of the effect of capital
availability upon transitions into self-employment 6 but, rather—and perhaps more relevant to policy—an estimate of the lasting effect upon the
stock of people running their own businesses. The inheritance effect is
found both at age 23 and at age 33. It is especially large among the
younger group (perhaps because older people have other ways to acquire
capital).
2. Consistent with the model developed, ISSP data reveal that surprisingly large numbers of people in the industrialized countries say they
would prefer to be self-employed, and NCDS data demonstrate that those
who are self-employed report themselves as more satisfied, ceteris paribus,
6
The usual reason that economists favor studies of transitions is because a
cross-section typically does not provide data on the timing of events. This reason
is inapplicable here: table 2 gives estimates using inheritances/gifts that were
received well before self-employment.
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What Makes an Entrepreneur?
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than employees. Complementary international evidence about the happiness of the self-employed is reported in appendix D.
3. Faced with the question, ‘‘Why did you not become self-employed?’’
the most common survey response given by a random sample of workers
in the BSA survey was to cite shortage of capital and money.
4. The NSS data indicate that most small businesses were begun not
with bank loans but with own or family money, that individual entrepreneurs felt they had needed most help with finance, and that the single
biggest concern of potential entrepreneurs was with where to obtain
capital.7
When this research began, a key motivation was to study the impact
of psychological traits on entrepreneurship. The NCDS data series is well
suited to this task, because it records the outcome of psychological tests
that were done during childhood. In practice, however, only one clear
correlation could be found. Although the effect is quantitatively small,
those who were anxious for acceptance (when children) were less likely
to run their own businesses at age 33. Using the variables available here,
psychology apparently does not play a key role in determining who
becomes an entrepreneur.
Appendix A
Proofs
Proofs of Propositions 1 and 2
These are immediate from differentiation of equation 2.
Proof of Proposition 3
It cannot be the case that p(k*) / i õ w, because entrepreneurs would
leave for the wage sector, which pays w. Thus, either marginal entrepreneurial utility, p(k*) / i, is equal to w, or, because people are held back
by capital constraints, it exceeds it. As p(k) is a decreasing function—it
is an array of decreasingly desirable projects—all other entrepreneurs
earn higher profit than the one operating the marginal project. Hence, all
but the marginal entrepreneur receives strictly more utility than regular
workers, and the marginal entrepreneur gets no less utility than regular
workers.
Proof of Proposition 4
The sum of entrepreneurs’ utilities is given by
*
k*
[p(k) / i]dk,
(A1)
0
and average entrepreneurial utility by
7
However, it should be recorded that in recent work Cressy (1996) reaches a
different conclusion.
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Blanchflower/Oswald
*
k*
0
[p(k) / i]dk
.
bZ
(A2)
Each worker in the wage sector gets utility equal to the wage w. There
are P 0 bZ individuals working in that sector. This is because the supply
of entrepreneurs is constrained to be the product of b (those with entrepreneurial vision) and Z (those with capital).
Assuming that the equilibrium is one where there is an aggregate shortage
of individuals with capital, free entry does not eliminate the difference in
returns to the marginal entrepreneur between the wage sector and the entrepreneurial sector. Let the average utility gap between the entrepreneurial
sector and the wage sector be denoted £. It is given algebraically by
vÅ
*
bZ
[p(k) / i]dk
0
0 f *(P 0 bZ ),
bZ
(A3)
where the latter term is the marginal product of labor in the wage sector.
A rise in Z , the total number of individuals with sufficient capital to run
their own business, increases the numbers setting up enterprises. This
drives down the marginal entrepreneurial return and, by inducing workers
to leave the wage sector, raises the marginal product of labor there. Hence
the utility difference, v, changes by
Ìv 1
1
Å [p(k*) / i] 0
ÌZ Z
bZ 2
*
bZ
[p(k) / i]dk / b f 9(P 0 bZ ). (A4)
0
The third of these three terms is unambiguously negative, by the concavity
of the production function, so to establish the proposition it is sufficient
to show that the first two terms sum to a negative number. Informally
this can be seen from the fact that the sum of these two terms equals one
over Z multiplied by the difference between the marginal entrepreneur’s
return and the average entrepreneur’s return. A more formal proof can
be produced by using a mean value theorem.
Appendix B
Further Data Sources
1. British Social Attitudes Survey Series, 1983–89
This series of surveys, core-funded by the Sainsbury Family Trusts,
was designed to chart movements in a wide range of social attitudes
in Britain. The data derive from annual cross-sectional surveys from a
representative sample of adults aged 18 or over living in private households in Great Britain whose addresses were on the electoral register.
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The first three surveys involved around 1,800 adults; the numbers were
increased to 3,000 in 1986. The sampling in each year involved a stratified
multistage design with four separate stages of selection. For further details
of the survey designs, nonresponses, etc., see Jowell, Witherspoon, and
Brook, British Social Attitudes, 1983, 1984, 1985, 1986, 1987, 1989, 1990.
2. National Survey of the Self-Employed, 1987
In February and March 1987 the British Department of Employment
commissioned a nationally representative sample of 12,000 British adults.
Interviews were then conducted with three subgroups drawn from this
initial sample: past, present, and future self-employed. In this article, we
focus on the latter two groups. They were selected according to the
following criteria: (1) current self-employed—adults who had become
self-employed in the previous 4 years, 1983–87, were still self-employed,
and had fewer than six employees (243 interviews); and (2) potential
self-employed—adults who said they were ‘‘seriously intending’’ to take
up self-employment in the next 12 months (139 interviews).
3. The International Social Survey Programme, 1989 and 1991
The International Social Survey Programme (ISSP) is a voluntary
grouping of study teams (11 in 1989 and 13 in 1991), each of which
undertakes to run a short, annual self-completion survey containing the
same set of questions in each country. The surveys are probability-based
national samples of adults. The topics change from year to year, with a
view to replication every 5 years or so. Surveys are currently available
for the years 1985–91.
4. The U.S. General Social Surveys, 1972–90
The General Social Surveys (GSS) have been conducted by the National
Opinion Research Center at the University of Chicago for the years
1972–90. There were no surveys in 1979 and 1981. Each survey is an
independently drawn sample of English-speaking persons 18 years of age
or over, living in noninstitutional arrangements within the United States.
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Appendix C
Table C1
Variable Definitions in the National Child Development Study (NCDS)
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Variable Definitions
1. Independent variables:
a) NCDS4:
Inheritance/gift
Unforthcoming score
Hostility score
Acceptance anxiety score
Father manager (õ25)
54
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Father: own account worker
Father: farmer employer
Father: farmer own account
Father: agricultural worker
County unemployment rate
Female
Apprenticeship
b) NCDS5:
Inheritance/gift
Unforthcoming score
Hostility score
Acceptance anxiety score
Father manager (õ25)
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Father: own account worker
Father: farmer employer
Father: farmer own account
Father: agricultural worker
Father: social class missing
Regional unemployment rate
Female
2. Dependent variables:
a) NCDS4:
Self-employed
b) NCDS5:
Self-employed
Year of NCDS
4
1T
1T
1T
2P
2P
2P
2P
2P
4
4
4
5
1T
1T
1T
2P
Description
The value of any inheritance or gift received above a threshold value (£)
Unforthcoming score in psychological test: 0 Å forthcoming
Hostility-to-children score in psychological test: 0 Å not hostile
Anxiety-for-acceptance-by-children score in psychological test: 0 Å not anxious
(1,0) dummy if father was a manager in central, local government, industry, or
commerce in an establishment employing õ25 people
(1,0) dummy if father worked ‘‘on his own account’’
(1,0) dummy if father was a farmer and employer
(1,0) dummy if father was a farmer on his own account
(1,0) dummy if father was an agricultural worker
The county unemployment rate in natural logarithms
(1,0) dummy if female
(1,0) dummy if the respondent had ever taken an apprenticeship
Mean
378.21
1.6588
.6658
.2994
.1209
.0341
.0108
.0098
.0131
2.4032
.4425
.1784
2P
2P
2P
2P
2P
5
5
The value of inheritance or gift received (£)
Unforthcoming score: 0 Å forthcoming
Hostility-to-children score: 0 Å not hostile
Anxiety-for-acceptance-by-children score: 0 Å not anxious
(1,0) dummy if father was a manager in central, local government, industry, or
commerce in an establishment employing õ25 people
(1,0) dummy if father worked on his own account
(1,0) dummy if father was a farmer and employer
(1,0) dummy if father was a farmer on his own account
(1,0) dummy if father was an agricultural worker
(1,0) dummy if father’s social class was missing
The region’s unemployment rate in natural logarithms
(1,0) dummy if female
1,563.0
1.6839
.6715
.3403
.0925
4
(1,0) dummy if the individual was self-employed in their main occupation in 1991
.0566
4
(1,0) dummy if the individual was self-employed in their main occupation in 1991
.1424
.0266
.0102
.0085
.0117
.1650
2.0750
.4341
NOTE. — All individuals were born in 1958. The numbers NCDS 1 – 5 denote the five sweeps of the survey undertaken since the initial birth study (the Perinatal Mortality
Survey), when the respondents were ages 7, 11, 16, 23, and 33. The most recent sweep was in 1991. The following letters indicate who completed the interview forms: P Å
parental response; T Å teacher response.
Appendix D
Table D1
Happiness Data from the International Social Survey
Programme, 1991
A. Question: ‘‘If you were to consider your life in general these
days, how happy or unhappy would you say you are on the
whole?’’
B. Responses
Employees
Not at all happy
Not very happy
Fairly happy
Very happy
N
1.50
8.69
63.50
26.32
4,548
Self-Employed
1.48
8.06
61.52
28.94
881
C. Ordered Probit Equation for Happiness: ISSP
Coefficient
Self-employed
Northern Ireland dummy
United States dummy
Italy dummy
Eire dummy
Israel dummy
Norway dummy
West Germany dummy
New Zealand dummy
Male
Age 25–34
Age 35–44
Age 45–54
Age 55–64
Age 65–74
Age ú75
Widowed
Divorced/separated
Never married
Threshold 1
Threshold 2
Threshold 3
Log likelihood
N
x2 (19)
Pseudo R2
Standard Error
.1042
.2487
.1723
0.6829
.2959
0.5237
0.3220
0.2396
0.1154
0.1360
0.1777
0.3256
0.3242
0.4438
.1414
0.0089
0.5575
0.5714
0.3964
02.9145
01.9584
.0723
.0446
.0784
.0651
.0721
.0739
.0716
.0653
.0664
.0696
.0330
.0573
.0608
.0651
.0749
.1467
.4228
.0992
.0645
.0456
.0891
.0790
.0748
04,665.5664
5,387
517.32
.0525
NOTE.—Excluded categories: Great Britain, õ25 years, married. Because of
missing values, N is slightly smaller than the sum of 4,548 and 881.
55
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Table D2
Happiness Data from the U.S. General Social Surveys,
1972–90
A. Question: ‘‘Taken all together, how would you say things are
these days—would you say that you are very happy, pretty
happy, or not too happy?’’
B. Responses
Employees
Not too happy
Pretty happy
Very happy
N
10.64
57.85
31.52
13,238
Self-Employed
8.77
53.50
37.72
1,983
C. Ordered Probit Equation for Happiness: GSS
Coefficient
Self-employed
Male
Age
Age squared
Married
Widowed
Divorced
Separated
Ever unemployed last 5 years
Black
Other nonwhite
Years of schooling
Threshold 1
Threshold 2
Log likelihood
N
x2 (36)
Pseudo R2
Standard Error
.0729
0.1230
0.0216
.0002
.3669
0.2843
0.1444
0.2203
0.1900
0.3253
0.0067
.0282
01.0829
.7158
.0285
.0194
.0046
.0000
.0279
.0570
.0385
.0561
.0312
.0293
.0677
.0033
.1108
.1107
013,749.05
15,221
1,034.08
.0369
NOTE.—Equation also includes 8 region dummies and 16 year dummies. Excluded
categories are single and white.
Appendix E
Table E1
Gift/Inheritance Equations at Age 23 (NCDS4)
Male
Married
Mother only dead
Father only dead
Both dead
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OLS,
Employed
(1)
OLS, All
(2)
Tobit,
Employed
(3)
Tobit, All
(4)
98.0
(1.40)
165.0
(2.32)
428.5
(2.14)
276.6
(2.15)
3,707.6
(6.89)
80.2
(1.36)
174.1
(2.97)
281.5
(1.88)
237.3
(2.43)
2,494.6
(6.60)
839.9
(1.99)
2,994.3
(6.83)
3,713.4
(3.48)
3,645.7
(5.15)
7,124.2
(3.04)
851.5
(2.29)
3,055.7
(8.02)
2,924.4
(3.37)
3,183.9
(5.52)
5,977.8
(3.31)
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What Makes an Entrepreneur?
57
Table E1 (Continued )
OLS,
Employed
(1)
Log unemployment rate
Father’s social class (NCDS2):
Employer/manager õ25
Professional, self employed
Professional, employee
Intermediate nonmanual
Junior nonmanual
Personal service
Foremen/super, manual
Skilled manual
Semiskilled manual
Unskilled manual
Own account worker
Farmer, employer/manager
Farmer, own account
Agricultural worker
Armed forces
N
F
R2/pseudo R2
x2
OLS, All
(2)
Tobit,
Employed
(3)
Tobit, All
(4)
0137.2
(.6)
0103.8
(.61)
0293.8
(.22)
0513.6
(.48)
0190.0
(.98)
550.1
(1.34)
302.0
(1.32)
0252.7
(1.17)
0201.6
(1.01)
0103.1
(.21)
0595.1
(2.78)
0542.2
(3.06)
0605.3
(3.13)
0665.3
(2.85)
0191.5
(.7)
1,337.0
(3.6)
0437.8
(1.12)
0625.5
(1.81)
0546.7
(1.51)
0298.3
(1.91)
254.1
(.82)
109.7
(.60)
0305.8
(1.76)
0378.4
(2.38)
0410.3
(1.07)
0694.9
(4.06)
0645.5
(4.57)
0678.4
(4.46)
0769.1
(4.34)
0362.2
(1.84)
1,142.7
(3.92)
0472.0
(1.47)
0715.2
(2.66)
0589.6
(2.07)
02,453.2
(2.58)
3,017.9
(1.67)
243.6
(.22)
03,413.2
(3.09)
04,355.3
(4.26)
04,672.0
(1.69)
07,105.7
(5.91)
08,233.9
(8.82)
08,618.8
(7.95)
010,791.7
(6.84)
04,192.9
(3.20)
1,917.4
(1.11)
04,006.8
(1.82)
09,146.7
(3.88)
04,638.1
(2.31)
02,404
(3.00)
1,941.9
(1.34)
0186.0
(.21)
02,817.1
(3.08)
04,702.4
(5.48)
06,199.1
(2.55)
06,867.2
(6.85)
07,646.0
(9.93)
8,351.8
(9.39)
10,544.7
(8.51)
04,528.2
(4.13)
1,941.5
(1.28)
02,717.6
(1.51)
09,197.2
(4.62)
03,791.9
(2.37)
10,155
5.78
.0245
7,503
7,503
6.49
.0222
.0181
398.9
10,155
.0194
547.04
NOTE.—Equations also include a constant and 10 region dummies. Columns 2 and 4 include 13
dummies for economic status at time of interview at age 23. t-statistics are in parentheses. Excluded
category: employer/manager ú25.
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